Research on Cooling-Load Characteristics of Subway Stations Based on Co-Simulation Method and Sobol Global Sensitivity Analysis

As high-energy-consumption underground public space, subway stations are responsible for a particularly significant proportion of air-conditioning energy use, especially during the cooling season, making the investigation of cooling-load characteristics highly important. However, the determination of independent influencing factors in different situations has not yet reached a consensus, and the role of interaction effects is lacking, which hinders the development of energy-saving strategies. For this purpose, this study proposes a sensitivity analysis framework based on 10 typical influencing factors from thermal parameters, meteorological parameters, internal heat disturbances, and indoor environmental setpoints. An input set was generated by integrating equal-step parameter discretization and Saltelli quasi-MonteCarlo sampling. A database containing 11,264 samples was constructed through an EnergyPlus–Python co-simulation method. Based on the Sobol global sensitivity analysis, the key influencing factors of subway station cooling load were identified and quantified, and the impact of these 10 factors was systematically analyzed. Results show that occupant density (SiT = 0.5605) and fresh air volume (SiT = 0.4546) are the dominant factors, contributing more than 50% of the load variance. In contrast, the characteristics of an underground structure significantly weaken the influence of the building-envelope heat transfer coefficient (SiT = 0.1482) and soil temperature (SiT = 0.0884). Furthermore, five groups of strong interaction effects were identified in this study, including occupant density–fresh air volume (Sij = 0.1094), revealing a nonlinear load response mechanism driven by multi-parameter coupling. This research provides a theoretical foundation and quantitative tool for the refined design and optimized dynamic coupled operation of underground transportation hubs.